Slawa Loev

This article provides an account of intuitions: Affectivism. Affectivism states that intuitions are emotional experiences. The article proceeds as follows: first, the features that intuitions are typically taken to have are introduced. Then some issues with extant theories are outlined. After that, emotional experiences and their central features are brought into view. This is followed by a comparison of intuitions and emotional experiences, yielding the result that emotional experiences fit and elucidate the feature profile of intuitions. Finally, it is specified what kind of emotional experiences intuitions are: intuitions are typically mild emotional experiences that belong to the subclass of epistemic feelings.

Emotion Review, Volume 14, Issue 3, Page 206-216, July 2022. This paper develops the claim that epistemic feelings are affective experiences. To establish some diagnostic criteria, characteristic features of affective experiences are outlined: valence and arousal. Then, in order to pave the way for showing that epistemic feelings have said features, an initial challenge coming from introspection is addressed. Next, the paper turns to empirical findings showing that we can observe physiological and behavioural proxies for valence and arousal in epistemic tasks that typically rely on epistemic feelings. Finally, it is argued that the affective properties do not only correlate with epistemic feelings but that we, in fact, capitalise on these affective properties to perform the epistemic tasks. In other words: the affective properties in question constitute epistemic feelings.

This paper aims to offer an account of affective experiences within Predictive Processing, a novel framework that considers the brain to be a dynamical, hierarchical, Bayesian hypothesis-testing mechanism. We begin by outlining a set of common features of affective experiences that a PP-theory should aim to explain: feelings are conscious, they have valence, they motivate behaviour, and they are intentional states with particular and formal objects. We then review existing theories of affective experiences within Predictive Processing and delineate two families of theories: Interoceptive Inference Theories and Error Dynamics Theories. We highlight the strengths and shortcomings of each family of theories and develop a synthesis: the Affective Inference Theory. Affective Inference Theory claims that valence corresponds to the expected rate of prediction error reduction. In turn, the particular object of a feeling is the object predicted to be the most likely cause of expected changes in prediction error rate, and the formal object of a feeling is a predictive model of the expected changes in prediction error rate caused by a given particular object. Finally, our theory shows how affective experiences bias action selection, directing the organism towards allostasis and towards optimal levels of uncertainty in order to minimise prediction error over time.